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Control and Synchronization of Neuron Ensembles Jr-Shin Li, Member, IEEE, Isuru Dasanayake, Student Member, IEEE, and Justin Ruths

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Control and Synchronization of Neuron Ensembles Jr-Shin Li, Member, IEEE, Isuru Dasanayake, Student Member, IEEE, and Justin Ruths IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 58, NO. 8, AUGUST 2013 1919 Control and Synchronization of Neuron Ensembles Jr-Shin Li, Member, IEEE, Isuru Dasanayake, Student Member, IEEE, and Justin Ruths Abstract—Synchronization of oscillations is a phenomenon neuroscience devising minimum-power external stimuli that prevalent in natural, social, and engineering systems. Controlling synchronize a population of coupled or uncoupled neurons, synchronization of oscillating systems is motivated by a wide range or desynchronize a network of pathologically synchronized of applications from surgical treatment of neurological diseases neurons, is imperative for wide-ranging applications from the to the design of neurocomputers. In this paper, we study the control of an ensemble of uncoupled neuron oscillators described design of neurocomputers [10], [11] to neurological treatment by phase models. We examine controllability of such a neuron of Parkinson’s disease and epilepsy [12]–[14]; in biology and ensemble for various phase models and, furthermore, study the chemistry, application of optimal waveforms for the entrain- related optimal control problems. In particular, by employing ment of weakly forced oscillators that maximize the locking Pontryagin’s maximum principle, we analytically derive optimal range or alternatively minimize power for a given frequency controls for spiking single- and two-neuron systems, and analyze entrainment range [8], [15] is paramount to the time-scale the applicability of the latter to an ensemble system. Finally, we present a robust computational method for optimal control of adjustment of the circadian system to light [16] and of the spiking neurons based on pseudospectral approximations. The cardiac system to a pacemaker [17]. methodology developed here is universal to the control of general Mathematical tools are required for describing the com- nonlinear phase oscillators. plex dynamics of oscillating systems in a manner that is Index Terms—Controllability, Lie algebra, optimal control, both tractable and flexible in design. A promising approach pseudospectral methods, spiking neurons. to constructing simplified yet accurate models that capture essential overall system properties is through the use of phase model reduction, in which an oscillating system with a stable I. INTRODUCTION periodic orbit is modeled by an equation in a single variable that represents the phase of oscillation [18], [19]. Phase models ATURAL and engineered systems that consist of en- have been very effectively used in theoretical, numerical, and N sembles of isolated or interacting nonlinear dynamical more recently experimental studies to analyze the collective components have reached levels of complexity that are beyond behavior of networks of oscillators [20]–[23]. Various phase human comprehension. These complex systems often require model-based control theoretic techniques have been proposed an optimal hierarchical organization and dynamical structure, to design external inputs that drive oscillators to behave in a such as synchrony, for normal operation. The synchronization desired way or to form certain synchronization patterns. These of oscillating systems is an important and extensively studied include multilinear feedback control methods for controlling phenomenon in science and engineering [1]. Examples include individual phase relations between coupled oscillators [24] and neural circuitry in the brain [2], sleep cycles and metabolic phase model-based feedback approaches for efficient control chemical reaction systems in biology [3]–[5], semiconductor of synchronization patterns in oscillator assemblies [9], [25]. lasers in physics [6], and vibrating systems in mechanical en- These synchronization engineering methods, though effective, gineering [7]. Such systems, moreover, are often tremendously do not explicitly address optimality in the control design large in scale, which poses serious theoretical and computa- process. More recently, minimum-power periodic controls tional challenges to model, guide, control, or optimize them. that entrain an oscillator with an arbitrary phase response Developing optimal external waveforms or forcing signals that curve(PRC)toadesired forcing frequency have been derived steer complex systems to desired dynamical conditions is of using techniques from calculus of variations [15], [26]. In this fundamental and practical importance [8], [9]. For example, in work, furthermore, an efficient computational procedure was developed for optimal control synthesis employing Fourier series and Chebyshev polynomials. Minimum-power stimuli d amplitude that elicit spikes of a single neuron Manuscript received January 03, 2012; revised August 14, 2012; accepted with limite January 09, 2013. Date of publication March 07, 2013; date of current version oscillator at specified times have also been analytically calcu- July 19, 2013. This work was supported in part by the National Science Foun- lated using Pontryagin’s maximum principle, where possible dation under the Career Award 0747877 and the Air Force Office of Scientific neuron spiking range with respect to the bound of the control Research Young Investigator Award FA9550-10-1-0146. Recommended by As- sociate Editor P. Tabuada. amplitude has been completely characterized [27], [28]. In J.-S. Li and I. Dasanayake are with the Department of Electrical addition, charge-balanced minimum-power controls for spiking and Systems Engineering, Washington University, St. Louis, MO asingle neuron has been thoroughly studied [29], [30]. 63130 USA (e-mail: [email protected]; [email protected]; [email protected]). The objective of this paper is to study the fundamental J. Ruths is with the Engineering Systems and Design Pillar, Singa- limit of how the neuron dynamics, described by phase models, pore University of Technology and Design, 138682 Singapore (e-mail: can be perturbed by the use of an external stimulus that is [email protected]). sufficiently weak. We, in particular, investigate controllability Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. and develop optimal controls for spiking an ensemble of Digital Object Identifier 10.1109/TAC.2013.2250112 neurons. In Section II, we briefly introduce the phase model 0018-9286/$31.00 © 2013 IEEE 1920 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 58, NO. 8, AUGUST 2013 for oscillating systems and examine controllability of an en- semble of uncoupled neurons for various commonly used phase models characterized by different baseline dynamics and phase response functions. Then, in Section III, we formulate the optimal control of spiking neurons as steering problems and de- rive minimum-power and time-optimal controls for single- and two-neuron systems. Finally, we implement a multidimensional pseudospectral method to solve optimal ensemble control prob- lems for spiking an ensemble of neurons, as is proven possible in Section II. This computational method permits us to further explore various objective functions with tunable parameters, which exchange, for example, performance and energy. The methodology developed in this article is universal to the control of general nonlinear phase oscillators. Fig. 1. Free evolution of a Theta neuron. The baseline current , and hence it spikes periodically with angular frequency and period . II. CONTROL OF NEURON OSCILLATORS similar nonlinear systems. Similar control problems of control- A. Phase Models ling a family of structurally similar bilinear systems have been extensively studied in quantum control [32]–[35]. The dynamics of an oscillator are often described by a set of ordinary differential equations that has a stable periodic orbit. B. Controllability of Neuron Ensembles Consider a time-invariant system with , In this section, we analyze controllability properties of where is the state and is the control, and finite collections of neuron oscillators. We first consider the assume that it has an unforced stable attractive periodic orbit Theta neuron model (Type I neurons) which describes both homeomorphic to a circle, satisfying superthreshold and subthreshold dynamics near a SNIPER , on the periodic orbit for (saddle-node bifurcation of a fixed point on a periodic orbit) . This system of equations can be reduced to bifurcation [36], [37]. asinglefirst-order differential equation, which remains valid 1) Theta Neuron Model: The Theta neuron model is charac- while the state of the full system stays in a neighborhood of its terized by the neuron baseline dynamics, unforced periodic orbit [19]. This reduction allows us to repre- , and the PRC, , namely, sent the dynamics of a weakly forced oscillator by a single phase variable that defines the evolution of the oscillation (2) (1) where is the neuron baseline current. If ,then for all . Therefore, in the absence of the input the neuron where is the phase variable, and are real-valued functions, fires periodically since the free evolution of this neuron system, and is the external stimulus (control) [19], [31]. The i.e., , has a periodic orbit function represents the system’s baseline dynamics and is known as the PRC, which describes the infinitesimal sensitivity for (3) of the phase to an external control input. One complete oscil- lation of the system corresponds to . In the case of neural oscillators, represents an external current stimulus and with the period and hence the frequency , is referred to as the instantaneous oscillation frequency in
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